Overview

Dataset statistics

Number of variables11
Number of observations250
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.6 KiB
Average record size in memory88.5 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:50:48.479119
Analysis finished2020-08-25 00:51:06.006737
Duration17.53 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz2 is highly correlated with oz1High correlation
oz1 is highly correlated with oz2High correlation
oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.5236437320709228e-09
Minimum-2.0413215160369877
Maximum2.0246267318725586
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:51:06.051267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.041321516
5-th percentile-1.579140973
Q1-0.7816294283
median-0.04223147035
Q30.7419174463
95-th percentile1.594945431
Maximum2.024626732
Range4.065948248
Interquartile range (IQR)1.523546875

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-656321408.3
Kurtosis-0.896800133
Mean-1.523643732e-09
Median Absolute Deviation (MAD)0.778742224
Skewness0.01560890068
Sum-3.80910933e-07
Variance1
2020-08-25T00:51:06.163000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.22265100510.4%
 
-0.883578181310.4%
 
0.858423471510.4%
 
1.64153325610.4%
 
0.478365510710.4%
 
-1.62856197410.4%
 
0.449085712410.4%
 
0.942055761810.4%
 
-1.68098199410.4%
 
1.89181244410.4%
 
0.0146402763210.4%
 
-0.518220305410.4%
 
-0.527681469910.4%
 
-0.428296983210.4%
 
0.264819860510.4%
 
-0.212804570810.4%
 
-1.03148341210.4%
 
1.38602697810.4%
 
-1.04659175910.4%
 
-1.09696066410.4%
 
-1.21602845210.4%
 
-0.908245205910.4%
 
-0.801894605210.4%
 
0.640758931610.4%
 
-1.84694063710.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-2.04132151610.4%
 
-2.01726913510.4%
 
-2.01409745210.4%
 
-2.00683021510.4%
 
-2.00359988210.4%
 
-1.9737869510.4%
 
-1.84694063710.4%
 
-1.72703373410.4%
 
-1.68098199410.4%
 
-1.67197704310.4%
 
ValueCountFrequency (%) 
2.02462673210.4%
 
1.98324358510.4%
 
1.90934753410.4%
 
1.89181244410.4%
 
1.79363250710.4%
 
1.77141642610.4%
 
1.74283933610.4%
 
1.68011999110.4%
 
1.67637419710.4%
 
1.64153325610.4%
 

oz2
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4475157260894776e-09
Minimum-1.700232982635498
Maximum1.6788511276245115
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:51:06.288773image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.700232983
5-th percentile-1.574197102
Q1-0.844536081
median0.002107570879
Q30.9269392937
95-th percentile1.50457688
Maximum1.678851128
Range3.37908411
Interquartile range (IQR)1.771475375

Descriptive statistics

Standard deviation0.9999999928
Coefficient of variation (CV)408577555.6
Kurtosis-1.215548124
Mean2.447515726e-09
Median Absolute Deviation (MAD)0.8819093406
Skewness-0.03938718258
Sum6.118789315e-07
Variance0.9999999855
2020-08-25T00:51:06.400028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.01269042510.4%
 
-0.352375805410.4%
 
-1.5130665310.4%
 
-0.886414706710.4%
 
0.147872567210.4%
 
0.410286158310.4%
 
-0.4757408510.4%
 
0.383145779410.4%
 
1.3035763510.4%
 
-1.2933170810.4%
 
-0.406750619410.4%
 
-1.69368934610.4%
 
0.959638476410.4%
 
0.182413846310.4%
 
0.856608271610.4%
 
-0.747717082510.4%
 
-0.45018649110.4%
 
1.36456334610.4%
 
-0.438622951510.4%
 
1.46710145510.4%
 
-1.3697700510.4%
 
0.971828997110.4%
 
-0.131140649310.4%
 
1.29033255610.4%
 
-0.446217536910.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.70023298310.4%
 
-1.69920718710.4%
 
-1.69749653310.4%
 
-1.69368934610.4%
 
-1.69098854110.4%
 
-1.68608272110.4%
 
-1.65887296210.4%
 
-1.64940524110.4%
 
-1.64925074610.4%
 
-1.6477303510.4%
 
ValueCountFrequency (%) 
1.67885112810.4%
 
1.64452588610.4%
 
1.62705326110.4%
 
1.62495005110.4%
 
1.61796259910.4%
 
1.60032045810.4%
 
1.59960341510.4%
 
1.59714055110.4%
 
1.57607686510.4%
 
1.56566417210.4%
 

oz3
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.6552006584523725e-09
Minimum-1.5925809144973757
Maximum1.8337846994400024
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:51:06.518897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.592580914
5-th percentile-1.474733639
Q1-0.9035370648
median0.007326759864
Q30.7969009131
95-th percentile1.629639512
Maximum1.833784699
Range3.426365614
Interquartile range (IQR)1.700437978

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-376619371.2
Kurtosis-1.185516429
Mean-2.655200658e-09
Median Absolute Deviation (MAD)0.8462611735
Skewness0.1106523748
Sum-6.638001646e-07
Variance1.000000005
2020-08-25T00:51:06.622355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.37676584710.4%
 
0.44978049410.4%
 
1.37729430210.4%
 
0.532396137710.4%
 
-1.34115874810.4%
 
0.803314566610.4%
 
0.700359642510.4%
 
-1.35384225810.4%
 
0.669726133310.4%
 
-1.27571284810.4%
 
-0.749409258410.4%
 
0.631017744510.4%
 
-1.21124851710.4%
 
-0.745269358210.4%
 
-0.0102104796110.4%
 
0.553857266910.4%
 
1.54810130610.4%
 
-0.907863736210.4%
 
-0.468333929810.4%
 
0.851703882210.4%
 
-1.10965156610.4%
 
1.35965037310.4%
 
-0.638808250410.4%
 
0.120256267510.4%
 
1.39284551110.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.59258091410.4%
 
-1.58209502710.4%
 
-1.55847954810.4%
 
-1.54654943910.4%
 
-1.54370009910.4%
 
-1.53399217110.4%
 
-1.52196145110.4%
 
-1.52000451110.4%
 
-1.51828360610.4%
 
-1.49991011610.4%
 
ValueCountFrequency (%) 
1.83378469910.4%
 
1.80970752210.4%
 
1.78417825710.4%
 
1.7613447910.4%
 
1.75930011310.4%
 
1.75505113610.4%
 
1.7451273210.4%
 
1.73438596710.4%
 
1.72807717310.4%
 
1.71919715410.4%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.313225746154786e-11
Minimum-1.76669180393219
Maximum1.6461193561553955
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:51:06.742261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.766691804
5-th percentile-1.559347337
Q1-0.8262185603
median-0.01127382927
Q30.8399041146
95-th percentile1.537759191
Maximum1.646119356
Range3.41281116
Interquartile range (IQR)1.666122675

Descriptive statistics

Standard deviation0.9999999981
Coefficient of variation (CV)-1.073741822e+10
Kurtosis-1.187462086
Mean-9.313225746e-11
Median Absolute Deviation (MAD)0.8283332288
Skewness-0.0205385403
Sum-2.328306437e-08
Variance0.9999999962
2020-08-25T00:51:06.852452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.72557675810.4%
 
0.258550524710.4%
 
-1.50819444710.4%
 
-1.30505514110.4%
 
-0.265959501310.4%
 
-0.0814433544910.4%
 
-0.782405197610.4%
 
0.54260164510.4%
 
0.543630003910.4%
 
0.700366914310.4%
 
-1.66828072110.4%
 
1.61849403410.4%
 
-1.19533324210.4%
 
1.12532746810.4%
 
-1.66731560210.4%
 
-0.453691244110.4%
 
-1.33131074910.4%
 
0.262170076410.4%
 
-1.37687408910.4%
 
1.35870504410.4%
 
0.492105841610.4%
 
1.25323247910.4%
 
-1.51885461810.4%
 
1.40557146110.4%
 
1.20731365710.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.76669180410.4%
 
-1.74972689210.4%
 
-1.72557675810.4%
 
-1.71306586310.4%
 
-1.68970704110.4%
 
-1.66828072110.4%
 
-1.6677124510.4%
 
-1.66731560210.4%
 
-1.6395635610.4%
 
-1.63684427710.4%
 
ValueCountFrequency (%) 
1.64611935610.4%
 
1.62850475310.4%
 
1.61849403410.4%
 
1.59671914610.4%
 
1.59319329310.4%
 
1.5884356510.4%
 
1.58308994810.4%
 
1.57794845110.4%
 
1.57538986210.4%
 
1.57178986110.4%
 

oz5
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0868534445762635e-09
Minimum-1.9101214408874512
Maximum1.609079122543335
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:51:06.964457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.910121441
5-th percentile-1.674334133
Q1-0.8697013557
median0.1332630143
Q30.792683363
95-th percentile1.466674793
Maximum1.609079123
Range3.519200563
Interquartile range (IQR)1.662384719

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)920087253.4
Kurtosis-1.117398687
Mean1.086853445e-09
Median Absolute Deviation (MAD)0.7549061105
Skewness-0.1919614076
Sum2.717133611e-07
Variance1.000000001
2020-08-25T00:51:07.070710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.06835818310.4%
 
-1.38408088710.4%
 
1.51873183310.4%
 
-0.957197964210.4%
 
1.04412984810.4%
 
1.47103166610.4%
 
0.832564890410.4%
 
0.46223843110.4%
 
-1.33430194910.4%
 
1.1309219610.4%
 
0.619297742810.4%
 
1.51235806910.4%
 
0.403007715910.4%
 
-0.654986023910.4%
 
-1.40199327510.4%
 
1.60792279210.4%
 
0.43804350510.4%
 
0.334933012710.4%
 
-0.629049062710.4%
 
1.21121466210.4%
 
-1.82058715810.4%
 
-1.06570136510.4%
 
0.736463367910.4%
 
-0.151522457610.4%
 
1.48153173910.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.91012144110.4%
 
-1.8659300810.4%
 
-1.82058715810.4%
 
-1.79640984510.4%
 
-1.79316461110.4%
 
-1.78088080910.4%
 
-1.77505612410.4%
 
-1.77136516610.4%
 
-1.75471663510.4%
 
-1.74294638610.4%
 
ValueCountFrequency (%) 
1.60907912310.4%
 
1.60792279210.4%
 
1.59607875310.4%
 
1.55181050310.4%
 
1.51873183310.4%
 
1.51235806910.4%
 
1.50501275110.4%
 
1.50285232110.4%
 
1.49984192810.4%
 
1.49615621610.4%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.691522240638733e-09
Minimum-1.7103198766708374
Maximum1.7307419776916504
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:51:07.186717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.710319877
5-th percentile-1.455476886
Q1-0.8839021176
median-0.131203562
Q30.8938833773
95-th percentile1.57139976
Maximum1.730741978
Range3.441061854
Interquartile range (IQR)1.777785495

Descriptive statistics

Standard deviation0.9999999996
Coefficient of variation (CV)371536963.2
Kurtosis-1.252314291
Mean2.691522241e-09
Median Absolute Deviation (MAD)0.8668711782
Skewness0.1048239574
Sum6.728805602e-07
Variance0.9999999993
2020-08-25T00:51:07.289137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.504923284110.4%
 
-0.13248036810.4%
 
-1.42850387110.4%
 
-1.71031987710.4%
 
1.56968903510.4%
 
-0.12992675610.4%
 
1.391948710.4%
 
0.910476684610.4%
 
-1.05923771910.4%
 
-0.162786319910.4%
 
-0.933258712310.4%
 
-0.946441471610.4%
 
-0.486647307910.4%
 
-0.00712315179410.4%
 
-0.583144187910.4%
 
-1.25363659910.4%
 
-0.941513776810.4%
 
0.0508612170810.4%
 
-0.53870594510.4%
 
-0.880501568310.4%
 
-1.28455007110.4%
 
1.33054792910.4%
 
-0.0277290958910.4%
 
1.05639421910.4%
 
0.74174606810.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.71031987710.4%
 
-1.67587387610.4%
 
-1.66306352610.4%
 
-1.6520482310.4%
 
-1.62695348310.4%
 
-1.60665082910.4%
 
-1.58950650710.4%
 
-1.56878662110.4%
 
-1.54051220410.4%
 
-1.51414203610.4%
 
ValueCountFrequency (%) 
1.73074197810.4%
 
1.73011982410.4%
 
1.70964825210.4%
 
1.7060588610.4%
 
1.70056366910.4%
 
1.69881510710.4%
 
1.67142486610.4%
 
1.66686475310.4%
 
1.65724265610.4%
 
1.65238070510.4%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.892928379656269e-10
Minimum-1.7007205486297607
Maximum1.742675542831421
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:51:07.407099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.700720549
5-th percentile-1.508918202
Q1-0.8263796866
median-0.06012387201
Q30.893899262
95-th percentile1.476506931
Maximum1.742675543
Range3.443396091
Interquartile range (IQR)1.720278949

Descriptive statistics

Standard deviation1.000000003
Coefficient of variation (CV)2568760340
Kurtosis-1.24658942
Mean3.89292838e-10
Median Absolute Deviation (MAD)0.9124290049
Skewness0.01945705341
Sum9.732320949e-08
Variance1.000000006
2020-08-25T00:51:07.509607image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.11193692710.4%
 
-0.710662901410.4%
 
0.738446295310.4%
 
-1.18587756210.4%
 
-1.00228059310.4%
 
-0.00477502355410.4%
 
1.4212176810.4%
 
-1.38020062410.4%
 
-1.05695545710.4%
 
0.500883638910.4%
 
-0.272293597510.4%
 
0.348073571910.4%
 
0.380447238710.4%
 
-1.34096610510.4%
 
-0.690319359310.4%
 
1.58232212110.4%
 
-1.50002849110.4%
 
-1.43916225410.4%
 
-1.1477470410.4%
 
0.903950393210.4%
 
-1.53312766610.4%
 
0.662248730710.4%
 
-0.388741463410.4%
 
-0.307794153710.4%
 
-1.21217942210.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.70072054910.4%
 
-1.69920325310.4%
 
-1.6881046310.4%
 
-1.68213045610.4%
 
-1.65484654910.4%
 
-1.65373325310.4%
 
-1.65006613710.4%
 
-1.56452870410.4%
 
-1.54057955710.4%
 
-1.53795206510.4%
 
ValueCountFrequency (%) 
1.74267554310.4%
 
1.73438525210.4%
 
1.73354804510.4%
 
1.70380997710.4%
 
1.70265841510.4%
 
1.62480378210.4%
 
1.60322594610.4%
 
1.58232212110.4%
 
1.54450964910.4%
 
1.50396764310.4%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.811452865600586e-10
Minimum-1.6014171838760376
Maximum1.6896847486495972
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:51:07.793450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.601417184
5-th percentile-1.50247215
Q1-0.9221777171
median0.0787101388
Q30.8757903278
95-th percentile1.522217321
Maximum1.689684749
Range3.291101933
Interquartile range (IQR)1.797968045

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-1720740104
Kurtosis-1.308091525
Mean-5.811452866e-10
Median Absolute Deviation (MAD)0.9124496281
Skewness0.02007194044
Sum-1.452863216e-07
Variance1.000000002
2020-08-25T00:51:07.904840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.806151449710.4%
 
1.54619109610.4%
 
-1.37339389310.4%
 
-1.59311795210.4%
 
-0.413170814510.4%
 
0.174480423310.4%
 
-0.999677956110.4%
 
0.412667125510.4%
 
0.877640128110.4%
 
-0.219766080410.4%
 
-0.0725290998810.4%
 
-1.59223413510.4%
 
-0.710419237610.4%
 
0.918118953710.4%
 
0.168835669810.4%
 
-1.34910786210.4%
 
1.23558187510.4%
 
0.240323126310.4%
 
-0.0362626202410.4%
 
-0.484489619710.4%
 
0.419256985210.4%
 
0.526986122110.4%
 
0.840462207810.4%
 
1.25585925610.4%
 
-0.466616392110.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.60141718410.4%
 
-1.59967255610.4%
 
-1.59962809110.4%
 
-1.59311795210.4%
 
-1.59223413510.4%
 
-1.57878744610.4%
 
-1.55865323510.4%
 
-1.54798245410.4%
 
-1.54633414710.4%
 
-1.53980779610.4%
 
ValueCountFrequency (%) 
1.68968474910.4%
 
1.65770101510.4%
 
1.64270627510.4%
 
1.62408864510.4%
 
1.60150384910.4%
 
1.59521973110.4%
 
1.59157586110.4%
 
1.58483862910.4%
 
1.5817130810.4%
 
1.57990086110.4%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.840533849024496e-09
Minimum-1.5810499191284182
Maximum1.909033298492432
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:51:08.017013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.581049919
5-th percentile-1.391866654
Q1-0.8634512722
median-0.05371719971
Q30.846802026
95-th percentile1.680619329
Maximum1.909033298
Range3.490083218
Interquartile range (IQR)1.710253298

Descriptive statistics

Standard deviation0.9999999983
Coefficient of variation (CV)-352046499.5
Kurtosis-1.199835229
Mean-2.840533849e-09
Median Absolute Deviation (MAD)0.8675299883
Skewness0.1936776525
Sum-7.101334623e-07
Variance0.9999999966
2020-08-25T00:51:08.132298image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.07448613610.4%
 
0.163000136610.4%
 
-0.845384478610.4%
 
-0.765429973610.4%
 
1.10482561610.4%
 
-1.23502433310.4%
 
0.887857496710.4%
 
-1.26594555410.4%
 
-0.407062351710.4%
 
-0.591954350510.4%
 
-0.564609289210.4%
 
-0.0631297007210.4%
 
1.12469744710.4%
 
0.353846758610.4%
 
0.216149359910.4%
 
-0.0285372752710.4%
 
0.13479562110.4%
 
-0.743797242610.4%
 
1.85323226510.4%
 
0.195713594610.4%
 
-0.844864070410.4%
 
-1.24538505110.4%
 
0.11473052210.4%
 
0.623178958910.4%
 
1.74928319510.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.58104991910.4%
 
-1.55385732710.4%
 
-1.53031754510.4%
 
-1.52979111710.4%
 
-1.51351904910.4%
 
-1.50504159910.4%
 
-1.50448727610.4%
 
-1.48834884210.4%
 
-1.48451650110.4%
 
-1.47023618210.4%
 
ValueCountFrequency (%) 
1.90903329810.4%
 
1.87294864710.4%
 
1.85323226510.4%
 
1.84373414510.4%
 
1.80915892110.4%
 
1.76404905310.4%
 
1.75757515410.4%
 
1.74928319510.4%
 
1.74617874610.4%
 
1.73308455910.4%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0265579205869243e-09
Minimum-1.740975260734558
Maximum1.6258444786071775
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:51:08.253769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.740975261
5-th percentile-1.636489624
Q1-0.8793465942
median0.170343861
Q30.9018402547
95-th percentile1.474127376
Maximum1.625844479
Range3.366819739
Interquartile range (IQR)1.781186849

Descriptive statistics

Standard deviation0.9999999999
Coefficient of variation (CV)493447529.8
Kurtosis-1.206181395
Mean2.026557921e-09
Median Absolute Deviation (MAD)0.8640570194
Skewness-0.1593230384
Sum5.066394801e-07
Variance0.9999999998
2020-08-25T00:51:08.365162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.10058426910.4%
 
0.700338900110.4%
 
1.55945181810.4%
 
0.321370303610.4%
 
-0.200012505110.4%
 
1.17024433610.4%
 
-1.54621970710.4%
 
0.932945370710.4%
 
-1.49097514210.4%
 
0.458522021810.4%
 
-0.992829918910.4%
 
-0.191199883810.4%
 
-1.70244467310.4%
 
1.33134925410.4%
 
-0.33415579810.4%
 
0.193588763510.4%
 
0.310603827210.4%
 
0.459891140510.4%
 
-1.59402692310.4%
 
1.54422128210.4%
 
1.14816594110.4%
 
0.171420022810.4%
 
-1.05885660610.4%
 
0.474917650210.4%
 
-1.00123035910.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.74097526110.4%
 
-1.73451256810.4%
 
-1.73448371910.4%
 
-1.72510564310.4%
 
-1.71169030710.4%
 
-1.70685815810.4%
 
-1.70252835810.4%
 
-1.70244467310.4%
 
-1.69613957410.4%
 
-1.6931972510.4%
 
ValueCountFrequency (%) 
1.62584447910.4%
 
1.62474954110.4%
 
1.61173641710.4%
 
1.57241451710.4%
 
1.57028508210.4%
 
1.56469285510.4%
 
1.55945181810.4%
 
1.54451215310.4%
 
1.54422128210.4%
 
1.51108002710.4%
 

target
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.4940818548202514e-09
Minimum-2.920823097229004
Maximum1.9995567798614504
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:51:08.483778image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.920823097
5-th percentile-1.706683737
Q1-0.7822771221
median0.1833977401
Q30.7385752946
95-th percentile1.396521807
Maximum1.99955678
Range4.920379877
Interquartile range (IQR)1.520852417

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-400949151
Kurtosis-0.575795681
Mean-2.494081855e-09
Median Absolute Deviation (MAD)0.6802890599
Skewness-0.4825830918
Sum-6.235204637e-07
Variance1.000000004
2020-08-25T00:51:08.583331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.03515493910.4%
 
0.0317676179110.4%
 
0.427096605310.4%
 
0.77508759510.4%
 
1.24841070210.4%
 
0.523341000110.4%
 
-1.60582709310.4%
 
-0.947929561110.4%
 
-1.71643662510.4%
 
0.403931617710.4%
 
0.819018006310.4%
 
1.49545311910.4%
 
-0.338216334610.4%
 
1.00325477110.4%
 
-0.269451439410.4%
 
-1.04816722910.4%
 
-1.45532059710.4%
 
0.211220696610.4%
 
0.364091366510.4%
 
0.061098694810.4%
 
-0.152748286710.4%
 
0.0787542015310.4%
 
0.591459810710.4%
 
0.527005195610.4%
 
-0.188085272910.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-2.92082309710.4%
 
-2.30281901410.4%
 
-2.27840805110.4%
 
-2.23778104810.4%
 
-2.15668058410.4%
 
-2.15657949410.4%
 
-2.08328962310.4%
 
-2.04330992710.4%
 
-1.95753550510.4%
 
-1.93280684910.4%
 
ValueCountFrequency (%) 
1.9995567810.4%
 
1.82213914410.4%
 
1.65140235410.4%
 
1.59686565410.4%
 
1.49888515510.4%
 
1.49818384610.4%
 
1.49545311910.4%
 
1.46772694610.4%
 
1.4578015810.4%
 
1.42912447510.4%
 

Interactions

2020-08-25T00:50:48.919615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:49.065500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:49.207016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:49.350721image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:49.485267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:49.628959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:49.763264image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:49.897369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:50.038891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:50.182283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:50.320617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:50.452003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:50.583409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:50.710121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:50.839387image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:50.967601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:51.100914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:51.229426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:51.357492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:51.483891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:51.617513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:51.748979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:51.868638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:52.003207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:52.135495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:52.440139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:52.574083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:52.712022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:52.851982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:52.985382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:53.119316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:53.256508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:53.389372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:53.520223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:53.660827image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:53.787654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:53.916754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:54.043784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:54.182965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:54.307187image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:54.431259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:54.560927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:54.692445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:54.817444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:54.943730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:55.081236image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:55.215329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:55.349396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:55.483035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:55.625010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:55.759760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:55.937762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:56.099415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:56.257288image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:56.556232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:56.682414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:56.814494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:56.936302image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:57.072463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:57.199859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:57.328758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:57.456958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:57.586193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:57.716384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:57.851883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:57.977770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:58.099679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:58.237561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:58.360007image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:58.487065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:58.611095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:58.748184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:58.877634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:59.002357image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:59.129375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:59.260250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:59.384116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:59.505698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:59.640045image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:59.765686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:59.893040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:00.016245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:00.146056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:00.443388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:00.568630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:00.701802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:00.842612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:00.972090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:01.095499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:01.236145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:01.374048image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:01.513032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:01.648340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:01.786332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:01.915487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:02.047017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:02.175780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:02.311573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:02.444126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:02.571233image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:02.704374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:02.838784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:02.972255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:03.097680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:03.226568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:03.356639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:03.488440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:03.615793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:03.747477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:03.883677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:04.007863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:04.131963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:04.428940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:04.548933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:04.668133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:04.794847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:04.914343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:05.038213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:05.159483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:05.284668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:05.407249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:51:08.712354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:51:08.936265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:51:09.159542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:51:09.378442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:51:05.632560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:51:05.902085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
01.2628371.3677410.2509391.194385-0.4262811.0420241.4141851.4196360.0714240.7270950.480223
10.4669520.1935630.302070-0.662642-1.308575-0.132480-1.5331281.1797220.809687-0.282259-1.187342
20.3861280.2677960.1392551.009296-0.392022-1.4201481.0535930.5269860.1850380.512320-0.428225
30.5361550.5728060.604992-1.0282790.462238-0.104650-1.439162-0.636967-1.2045360.373467-1.689547
4-1.518207-1.354855-0.6487911.1055110.073048-0.7824881.742676-1.2454150.7944160.1935890.135833
50.3415830.1440980.010692-1.1292190.438044-0.4781811.6248041.1075690.802662-1.259399-0.645953
60.8196841.2500010.008733-1.636844-0.700574-1.387322-0.272294-0.0725290.8865890.571122-2.083290
71.2069770.6010311.719197-1.403596-1.519120-0.845983-0.3887410.258186-0.930300-1.576993-2.156579
8-0.205958-0.423698-0.0136160.4301920.634986-0.2979000.738446-1.409888-1.231850-0.8959381.014112
9-1.398865-1.369770-0.405049-0.2527911.1553921.1551791.7343851.4119081.0636011.207383-0.077087

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
240-2.006830-1.649405-1.518284-0.8798690.5193811.119448-0.400449-0.843170-0.0285370.171420-0.075856
241-2.003600-1.693689-0.9807601.4142651.2563351.372130-1.535872-1.072072-1.2198841.5594520.580195
242-1.507372-1.526494-0.3673561.583090-0.580396-1.540512-1.269655-0.833076-0.947122-1.6793740.088787
2430.6275861.373261-1.2854800.507399-1.820587-1.3424910.5445610.6612041.044935-0.881370-1.213133
244-0.3933170.244696-1.3664641.1208880.619298-1.1930141.1119370.962382-0.3687780.2059901.651402
245-0.0790630.149709-1.0614110.007995-0.629049-1.363197-1.500028-0.1570280.2026500.5821980.403932
246-0.510637-0.459725-0.314452-1.5081941.5187320.334832-0.3077940.057009-1.505042-0.3006250.498274
247-0.028460-0.5272150.684542-1.474210-1.080138-0.9464411.4645891.581713-0.146230-1.696140-0.219763
2481.4562921.3035761.641846-1.0897620.403008-0.942314-1.0022810.6523721.217530-0.4258180.381331
249-0.179755-0.2966430.492243-0.407696-1.286957-0.5044881.1284740.6285110.1347960.9033950.149265